Prediction of academic achievement using the School Motivation Analysis Test.

Transcription

1 Bond University From the SelectedWorks of Gregory J. Boyle 1989 Prediction of academic achievement using the School Motivation Analysis Test. Gregory J. Boyle, University of Melbourne Brian K Start, University of Melbourne John E Hall, University of Melbourne Available at:

2 Prediction of Academic Achievement using the School Motivation Analysis Test Gregory J. Boyle, K. Brian Start, and E. John Hall University of Melbourne Address correspondence to Gregory J. Boyle, PhD, University of Melbourne, Parkville VIC 3052, Australia.

3 Abstract Mathematics and English achievement was investigated among 277 Year 10 Australian students. Using the School Motivation Analysis Test (SMAT) as the measure of dynamic motivational traits, significant achievement variance (25 percent for Mathematics; 34 percent for English) was accounted for independently from that due to abilities and personality traits. Male students tended to invest a greater proportion of intellectual abilities (indexed via the SMAT General Information Intelligence score) than did females in the learning of Mathematics (accounting for 25 percent of the variance for males), whereas females demonstrated a higher investment of abilities in English. The specific motivational dynamic traits predictive of performance in each subject area are reported and discussed.

4 Introduction Considerable research (e.g., Dielman et al., 1971, 1973; Cattell et al., 1972; Cattell and Child, 1975; Cattell and Kline, 1977; Gillis and Lee, 1978; Boyle, 1983a, 1983b) has suggested that motivational dynamic traits are important along with abilities and personality traits in predicting academic school learning. Cattell (1985) suggested that intra-personal motivational structure comprises organically based (innate) drives (which he termed "ergs") as well as culturally acquired structures ("sems"). The latter develop from experience through exposure to sociocultural institutions such as family, church and school. The School Motivation Analysis Test (SMAT-Krug, et al., 1976) is an objective instrument designed specifically to quantify 10 of the most important, factor analytically derived, dynamic traits (ergs and sems) among secondary school students. The SMAT comprises subscales at both the integrated (expressed at the conscious level in daily life) and the unintegrated (drives at the unconscious/unexpressed) levels. The six ergs are labelled Assertiveness, Mating (sexual drive), Fear, Narcism (self-preoccupation), Pugnacity and Protectiveness. The four sems are labelled Self Sentiment, Superego, School and Home. It is important in measuring motivation to utilise objective: rather than self-report instruments, given the transparency of the latter items, making them prone to response distortion (Boyle, 1985c). Dynamic traits should relate significantly to school achievement. Kline (1979) reported that several integrated factors correlated significantly with mathematics performance, including Mating (0.42), Self-Sentiment (0.32),

5 Pugnacity (0.28) and Assertiveness (0.27); As Gillis and Lee (1978) have pointed out, for both reading and mathematics, motivational dynamic factors may account for up to percent of the achievement variance. From Kline's work, it would appear that most of the motivational predictive variance is associated with the integrated/conscious components, rather than with the unintegrated/unconscious ones. As for SMAT reliability and validity, Boyle and Cattell (1984) have reported immediate MAT test-retest (dependability) coefficients ranging from 0 51 to 0.81 (mean 0.66), while stability estimates (for retest over a five-week interval) averaged Even though the SMAT (and the related MAT instrument) was designed to measure somewhat fluctuating and unstable dynamic motivational traits, nevertheless, Krug et al. (1976) have reported test-retest reliability coefficients for the SMAT derivative scores ranging from 0.92 to 0.94, thereby suggesting the reliability of the instrument. Validity has been well documented (Kline and Grindley, 1974; Cattell and Child, 1975; Birkett and Cattell, 1978; Boyle, 1983c, 1984, 1985a, 1985b; Boyle and Cattell, 1984; Cattell, 1985). Cohen (1978) pointed to factor validities ranging from 0.70 to 0.92 for the 20 (unintegrated and integrated) primary subscales. Boyle (1983b, 1986, 1987), and Boyle and Cattell (1987) have demonstrated that motivational factors do account for significant variance in school learning. Kline (1979) has singled out the Self- Sentiment and Superego factors as being central to school learning, and has labelled them "master sentiments". The present study assesses further the relationships between motivational dynamic traits and achievement in mathematics and English.

6 Method Subjects The sample comprised 277 students (99 males, 178 females) whose mean age was 15.4 years (SD = 0.5 years). All students were enrolled in Year 10 at one of four secondary schools selected to represent as diverse a range of students as possible within the limits of the study. The four schools included a metropolitan boys' technical school, a suburban high school, a country high school, and a metropolitan girls' high school. There was a wide spread of students from various sociocultural backgrounds represented in the sample. While all students were free to participate or withdraw from the study at any time, in practice virtually all cooperated fully. Procedure and rationale Mathematics and English were compulsory subjects at Year 10 level in all four schools involved. Questions used for the mathematics measure were taken from the Australian Mathematics Item Bank (AMIB) (ACER, 1978), while the English Skills Assessment Test (ESAT) (ACER, 1981) served as the other measure. To avoid "position effects", presentation of measures was counterbalanced (i.e., Mathematics/English/SMAT, English/SMAT/ Mathematics, SMAT/Mathematics/English) in the different classes tested. AMIB items were classified according to the specific content area and cognitive skills of Year 10 students. ESAT items were selected from the Sequential Tests of Educational Progress (STEP) - Series II, and from the Descriptive Tests of Language Skills (DTLS).

7 Results and Discussion Product-moment correlations of English and Mathematics with all SMAT variables (10 unintegrated (U) and 10 integrated (I) primary scores; 10 conflict (U- I) scores; 10 total motivation (U +I) scores; and five derivative scores labelled: Total Autism-Optimism, General Information Intelligence (Gil), Total Integration, Total Personal Interest, and Total Conflict) are presented in Table I. Some 22 of the correlations with Mathematics were significant, while 28 scores were significantly correlated with English scores. The results suggest that Assertiveness may facilitate English performance, but have little effect on Mathematics. Mating (sex drive) appears to have a negative impact on achievement in both Mathematics and English, particularly if there is unintegrated (unexpressed) and unresolved conflict in this area. Fear at the unintegrated level may negatively influence both subject areas, although at the integrated level, it seems to benefit mainly English. The conflict Fear score is negatively related to English performance. Narcism is negative for Mathematics at the unintegrated level, but positive at the integrated level across both subject areas. Unresolved Narcistic conflict seems to negatively influence Mathematics achievement. Even though Pugnacity has a slight positive effect on achievement at the integrated level, both the conflict and unintegrated components seem to have a pronounced negative impact on both subject areas. Self-Sentiment relates positively to both English and Mathematics performance, as does Superego, as expected from Kline's (1979) findings. However, the Superego conflict score has a clearcut negative impact on both Mathematics and English achievement. As expected, integratedschool correlates positively with achievement scores, while the conflict component

8 has a negative impact. Four of the five SMAT derivative indices correlate significantly with performance in both subject areas. Interestingly, the integrated components of Assertiveness, Mating, Fear, and Self Sentiment are all positively related to English performance but not to Mathematics. Generally, both the unintegrated and conflict components of several of the dynamic traits exhibit a negative association with achievement scores, while the integrated and total motivation components seem to relate positively to achievement in both subject areas. Therefore, motivational dynamic traits may contribute positively to academic achievement to the extent that they are integrated/expressed in the individual's daily life. On the other hand, academic achievement may be adversely influenced in direct proportion to the degree of motivational tension which is unexpressed in the individual's daily life, and to the extent that there is an imbalance between the unintegrated and integrated components, with the former predominating Table Prediction of school achievement from SMAT variables was examined further using multiple regression analysis (stepwise forward) procedures. Results for the prediction of Mathematics performance for the total sample are presented in Table 2. The five significant SMAT predictors accounted for 25 percent of the Mathematics variance. The derivative variable Gil accounted for 15 percent of the achievement variance, while I-School accounted for 5 percent of the variance.

9 General Information Intelligence (Gil) accounted for more of the variance than did any of the purely motivational variables. However, as Table 3 shows, the variance accounted for when Gil was removed from the equation was still 25 percent, suggesting that the purely motivational SMAT variables are to some extent predictive of Mathematics achievement Tables 2 & Among male students, Gil was the best predictor of Mathematics performance accounting for 32 percent of the variance, whereas for females, Gil was not among the significant predictors at all. While the achievement variance in Mathematics accounted for in the male sample was 46 percent, it was 18 percent in the female sample. As for achievement in English, 35 percent of the variance was accounted for in the total sample. Again, Gil was the most significant predictor, accounting for 24 percent of the variance (Table 4). When Gil was excluded from the analysis, the purely motivational variables still explained 34 percent of the variance in English (Table 5). GII was nevertheless, the single most significant predictor for both males and females, accounting for 41 percent and 51 percent of the variance respectively. Female students invested greater ability in English than did males, who invested more ability in Mathematics in general. These sex differences across the two subject areas have important implications for educational practice (Benbow and Stanley, 1982).

10 Tables 4 & Separate regression analyses on unintegrated and integrated SMAT variables indicated that the unintegrated dynamic traits accounted for only 13 percent of the variance in Mathematics for males, and 7 percent for female students. The unintegrated dynamics accounted for 20 percent of the English variance among males, and 15 percent among females. For the integrated dynamics, 40 percent of the Mathematics variance was predicted for the male students, whereas 12 percent was accounted for by the female students. Of the variance for all variables and both sexes, 80 percent was accounted for by the integrated dynamic traits alone, whereas the unintegrated dynamics explained only 8 percent of the total variance. A similar pattern emerged in English, with the integrated dynamics accounting for 77 percent of the total variance. On this evidence, unconscious/unintegrated motivational factors play little role in academic achievement, in contrast to the integrated factors which appear to be significant predictors of academic learning outcomes. To investigate further motivational variables and school achievement, an iterative principal factoring of the SMAT intercorrelations as well as the two achievement variables was carried out for the total sample (Table 6). Initial communality estimates (SMCs) were iterated until convergence occurred at the third decimal place. Factor extraction by the Scree test (Cattell, 1978; Hakstian, et al., 1982) suggested five significant factors which were rotated to simple structure using SPSS (cf. Gorsuch, 1983).

11 The two achievement variables loaded significantly only on Factor 1, which had significant loadings on Gil, Integrated, Total and Conflict Superego, Total Self-Sentiment, Conflict and Unintegrated Fear, Total Interest (cf. Boyle, 1983a), Total Integration, Total Conflict, Total and Integrated Assertiveness. These are the motivational variables involved in Mathematics and English performance in the present study. On the present evidence, achievement is influenced by general ability (Gil loaded 0.85 on Factor 1), a keen interest in things, and an integrated, conflict-free view of oneself. These findings support those of Cattell et al. (1972), Dielman et al., (1971, 1973), as well as Cattell and Child (1975) who previously had shown that SMAT variables can contribute significantly to the prediction of academic school achievement Table References ACER (1978). Australia Mathematics Item Bank. Hawthorn, Vic.: Australian Council for Educational Research. ACER (1981). English Skills Assessment Test. Hawthorn. Vic.: Australian Council for Educational Research. Benbow, C. P., and Stanley, J. C. (1982). Consequences in high school and college of sex differences in mathematical reasoning ability: a longitudinal perspective. Am. educ. Res. J., 19,

12 Birkett, H., and Cattell, R. B. (1978). Diagnosis of the dynamic roots of a clinical symptom by P-technique: a case of episodic alcoholism. Multivar. exp. clin. Res., 3, Boyle, G. J. (1983a). Critical review of state-trait curiosity test development. Motivation and Emotion, 7, Boyle, G. J. (1983b). Effects on academic learning of manipulating emotional states and motivational dynamics. Br. J. educ. Psychol., 53, Boyle, G. J. (1983c). Higher-order factor structure of Cattell's MAT and 8SQ. Multivar. exp. clin. Res., 6, Boyle, G. J. (1984). Effects of viewing a road trauma film on emotional and motivational factors. Accident Analysis and Prevention, 16, Boyle, G. J. (1985a). A reanalysis of the higher-order factor structure of the Motivation Analysis Test and the Eight State Questionnaire. Person. indiv. Dijf., 6, Boyle G. J. (1985b). A reconsideration of the Cooper/Kline critique of the factor structure of the Motivation Analysis Test. Multivar. exp. clin. Res., 7, Boyle, G. J. (1985c). Self-report measures of depression: some psychometric considerations. Br. J. clin. Psychol., 24, Boyle, G. J. (1986). Prediction of academic achievement from intrapersonal psychological variables. Paper presented at the annual conference of the Australian Association for Research in Education, Ormond College, University of Melbourne, November - Abstract in Austr. educ. Researcher, 14, 41.

13 Boyle, G. J. (1987). Commentary: the role of intrapersona1 psychological variables in academic school learning. J. school Psychol., 25, Boyle, G. J., and Cattell, R. B. (1984). Proof of situational sensitivity of mood states and dynamic traits-ergs and sentiments- to disturbing stimuli. Person. indiv. Diff., 5, Boyle, G. J., and Cattell, R. B. (1987). A first survey of the similarity of personality and motivation prediction of "in situ" and experimentally controlled learning, by structured learning theory. Aust. Psychologist; 22, Cattell, R. B. (1978). The Scientific Use of Factor Analysis in Behavioral and Life Sciences. New York: Plenum. Cattell, R. B. (1985). Human Motivation and the Dynamic Calculus. New York: Praeger. Cattell, R. B., and Child, D. (1975). Motivation and Dynamic Structure. New York: Wiley- Halsted. Cattell, R. B., and Kline, P. (1977). The Scientific Study of Personality and Motivation. New York: Academic Press. Cattell, R. B., Barton, K., and Dielman, T. E. (1972). Prediction of school achievement from motivation, personality and ability measures. Psychol. Rep., 30, Cohen, J. (1978). Review of the School Motivation Analysis Test. In Buros, O. K. (Ed.), Eighth Mental Measurements Yearbook. Highland Park, N.J.: Gryphon.

14 Dielman, T. E., Barton, K., and Cattell, R. B. (1971). The prediction of junior high school achievement from objective motivation tests. Personality, 4, Dielman, T. E. Barton, K., and Cattell, R. B. (1973). The Prediction of Junior High School Grades from the Culture Fair Intelligence Test and Objective Measures of Motivation. Champaign, Ill.: Institute for Personality and Ability Testing. Gillis, J. S., and Lee, D. C. (1978). Second-order relations between different modalities of personality trait organisation. Multiv. exp. clin. Res., 3, Gorsuch, R. L. (1983). Factor Analysis. (Revised.) Hillsdale, N.J.: Erlbaum. Hakstian, A. R., Rogers, W. T., and Cattell, R. B. (1982). The behaviour of number-of-factors rules with simulated data. Multiv. behav. Res., 17, Kline, P. (1979). Psychometrics and Psychology. London: Academic Press. Kline, P., and Grindley, J. (1974). A 28-day case-study with the MAT. J multiv. exp. Person. clin. Psychol., 1, Krug, S. E., Cattell, R. B., and Sweney, A. B. (1976). Handbook for the School Motivation Analysis Test (SMAT). Champaign, Ill.: Institute for Personality and Ability Testing.

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